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Experiential exercises with four production planningand control systemsAndrew Manikasa, Mahesh Guptaa & Lynn Boyda

a College of Business, Management Department, University of Louisville, Louisville, KY, USAPublished online: 27 Nov 2014.

To cite this article: Andrew Manikas, Mahesh Gupta & Lynn Boyd (2014): Experiential exercises with four production planningand control systems, International Journal of Production Research, DOI: 10.1080/00207543.2014.985393

To link to this article: http://dx.doi.org/10.1080/00207543.2014.985393

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Experiential exercises with four production planning and control systems

Andrew Manikas*, Mahesh Gupta and Lynn Boyd

College of Business, Management Department, University of Louisville, Louisville, KY, USA

(Received 17 July 2014; accepted 1 November 2014)

In the authors’ experience, students have difficulty in understanding the differences between production planning andcontrol techniques. Presumably, current business managers, although they have heard about these systems, may also lackclarity on the differences between them. We outline manual games for simulating production runs in four systems(Materials Requirements Planning, Just-In-Time, Theory of Constraints and CONWIP) to give managers and students’insight into the mechanics of different production planning control techniques. We then provide excel-based simulationtools to allow users to vary parameters for each system and see the impact on inventory and throughput. We believe thatthe combination of manual and excel-based games significantly enhances understanding of the systems as well as theirdifferences.

Keywords: production planning; pedagogy; simulation

1. Introduction

Production planning and control (PPC) systems are key factors in the success of a manufacturing organisation. Given theimpact the choice of PPC system has on production capabilities, it is not surprising that a number of systems, e.g. MaterialsRequirements Planning (MRP), Just-In-Time (JIT), Theory of Constraints (TOC) and CONWIP, have been used in practiceand studied by researchers. MRP is a push system where the scheduled materials are released to the production environ-ment with no specific limits on work-in-process inventory (WIP). WIP inventory consists of materials where value has beenadded to raw materials, but the item is not yet in the finished goods state that can be sold to a customer. CONWIP,developed by Spearman, Woodruff, and Hopp (1989), seeks to maintain a constant WIP level in the production system.TOC identifies the constraint operation and keeps WIP at strategic buffers to ensure the system effectiveness.

There has been a great deal of interest in these various PPC approaches out of a concern for improving manufactur-ing performance. Academicians have researched their virtues, practitioners have commented on their capabilities or limi-tations and learners have trouble discerning the peculiar characteristics of these approaches. Several researchers haveargued that the underlying characteristics of such approaches can be integrated. However, the underlying characteristicsclearly distinguishing these approaches have not been well researched and well understood and this paper aims to makea contribution in this regard (e.g. Benton and Shin 1998; Gupta and Snyder 2009).

In the educational arena, the role of experiential exercises in helping students and managers understand PPC con-cepts has also long been recognised (Ammar and Wright 1999; Heineke and Meile 1995; Muckstadt and Jackson 1995;Sterman 1992; Wu 1989). In The Goal, Goldratt and Cox (2012) introduce two important aids to learning – the meta-phor of the Boy Scout hike and the dice game, an experiential exercise. Both the manual dice game described in TheGoal (Goldratt and Cox 2012) and also by other authors (Cox and Walker 2005; Hill 2000; Umble and Srikanth 1990)and computerised versions (Gupta and Boyd 2011; Johnson and Drougas 2002; Lambrecht et al. 2012; Umble andUmble 2001) have been presented to help students and managers understand the impact of dependencies and variabilityon system performance. However, in the authors’ experience, students often have a difficult time grasping thedifferences among the four PPC systems.

The purpose of this paper is to describe a set of models that simulate MRP, JIT, TOC and CONWIP systems. Themodels can be run either manually, with dice and pennies, or in excel. The recommendation we make is that the manualand Excel-based models be used in combination to enhance student learning. The manual models allow students todevelop a deep understanding of the differences in how the four PPC systems operate. The excel models are then usedto simulate 1000 runs to allow students to compare the performance of the PPC systems with respect to performance

*Corresponding author. Email: Andrew.manikas@louisville.edu

© 2014 Taylor & Francis

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measures. This combined approach, together with discussion of the results requiring students to answer deep questionsabout the systems, has been shown by research to enhance student learning (Dunlosky et al. 2013; Pashler et al. 2007).

The rest of the paper is organised as follows: First, we explain briefly the major characteristics of the four PPC sys-tems. Next, we describe the manual versions of the dice game for each PPC system. Third, we describe a set of Excel-based dice game models of the systems that illustrate the key differences between these approaches. In addition, weshare our experience using these models in a classroom setting with both undergraduate and MBA (Master of BusinessAdministration) students. Lastly, we suggest some future research directions.

2. PPC systems: The Boy Scout metaphor

MRP has been used since the 1960s, when availability of computers first made the huge number of calculations requiredby MRP logic possible. MRP ‘pushes’ material into the system and through the production process in an effort to runall operations at maximum efficiency, as the system emphasises efficiency over manufacturing lead time. In Figure 1below, using the hike metaphor from The Goal (Goldratt and Cox 2012), the five scouts are walking towards the left,each at his own pace. Scout #3 (the diamond), is carrying all the equipment and is therefore slower than the others. Thehiking trail is single file, so scouts #4 and #5 cannot pass him. Over time, scouts #1 and #2 will get farther and fartherahead of scout #3, spreading out the troop along the trail. The space between scouts is analogous to the increasingamount of inventory that would have to be released into the system to allow every machine to operate (i.e. walk) at itsown natural pace.

JIT was developed by Toyota in the 1950s and was first used in the US by plants of Japanese manufacturers in the1970s. JIT is a pull system, which means that each operation only produces the quantity of products requested by thenext downstream operation, and ultimately by the final customer. A Kanban card is frequently used to control the flowof parts through the system. When a customer, either the end customer or a downstream operation, wants a product, thekanban card is sent to the previous (upstream) operation and that operation produces the product, which in turn triggersa need for the next upstream operation to also produce another unit of product. A JIT system is characterised by lowWIP inventories situated throughout the plant to the left of each operation. The second row of scouts in Figure 1 depictsa JIT (or lean) situation. Each scout is tied to the next scout with a fixed length of rope (the kanban quantity would bethe length of the rope). A scout cannot get farther ahead than the length of the rope tying him to the scout behind him.In this illustration, scout #2 wants to go faster than scout #3 (the slowest one), but cannot go farther away than thelength of the rope. Similarly, scout #1 can go no farther away from scout #2. The faster scouts cannot pull or push scout#2. Scouts #4 and #5 can lessen the distance between themselves and scout #3, as indicated by the slack ropes. Therope between each pair of scouts is analogous to a maximum inventory allowed between workstations.

Figure 1. The four PPC methods illustrated as hikers.

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The TOCs PPC system is referred to as drum–buffer–rope (DBR). The drum of the system refers to the schedule ofthe slowest operation in the process, i.e. the constraint. The buffer is the amount of inventory allowed into the systembefore the constraint, and the rope is the flow of information from the constraint to the material release operationrequesting the release of raw material into production. In a DBR system, every effort is made to increase the efficiencyof the constraint even at the detriment of other measures. The buffer ensures that the constraint never runs out of work.The rope mechanism restricts all inventory not needed to protect the constraint from entering the system. Each operationis directed to work as fast as it can when there is work to do and to sit idle if there is no material to work on. InFigure 1, the third row shows that our scout #3 is the drum. By tying a rope from #3 to the lead scout #1, no scout cango faster than the slowest scout. Scout #2, although not tied by a rope, cannot pass scout #1, who is held by the ropetie to scout #3.

CONWIP is a hybrid system introduced in 1990 by Spearman, Woodruff and Hopp. CONWIP combines features ofJIT and TOC; however, it assumes that there is no dominant constraint (or it cannot be determined). Instead, dependingon the level of variation in the system, some ‘desired’ level of WIP is set for the whole system. Once the desired WIPlevel for the system is set, the release of raw material to the first operation is controlled by the number of units shippedto the end customer. The fourth row in Figure 1 shows CONWIP. The last scout is tied to the first scout. This ensuresthat all scouts are somewhere between the first and last scout in a single file at most the length of the rope. The rope inthe CONWIP system represents the total amount of inventory in the system.

3. The manual dice game

Having students play versions of the dice game representing each of the PPC systems manually is an excellent way toget them to appreciate the differences in the four approaches. In this section, we describe in some detail how the manualdice game can be played to mimic MRP, JIT, TOC and CONWIP systems.

The manual dice games simulate a small production line. In the line, workstation cycle times are almost balanced,which means that, in any given period of time, each workstation can theoretically produce almost the same number ofparts. Of the five workstations in the line, the third workstation takes slightly longer to process each part.

To play the game, the student should have five markers to represent the five workstations. These may be as simpleas five post-it notes with the numbers 1 to 5 written on them. The student also needs 100 pennies (or other items, suchas paper clips, matches, etc.) to represent pieces of inventory, and one six-faced die to determine the production for eachworkstation in turn. The student should print four copies of the manual worksheet in the appendix to use in the fourgames.

Set up the workstation post-it notes in order from left to right leaving space between them for pennies (representinginventory). The third workstation is slightly slower than the others, so somehow make that post-it note look different(different colour, or circle the number 3 written on it). See Figure 2, Step a.

Each workstation will process a quantity of parts up to the number showing on the die face. However, forworkstation three, the maximum number of parts to be processed is the number on the die face minus one.

Use the four forms (one for each method below) from the appendix to record the inventory results as you roll thedie for each workstation on each of the rounds.

3.1 Material requirements planning

MRP is known as a push system because material is pushed down the line as it is processed by each workstation.Because the first workstation can process up to six parts (the maximum roll of the die) make sure there are six penniesto the left of workstation 1 at the beginning of each round. We want to investigate this system as if it had been alreadyprocessing material previously. A slower resource will tend to have material build up to the left of it since that worksta-tion cannot process parts as quickly as the workstations to the left of it. To ‘warm up’ this system, place four penniesbetween workstations 1 and 2, six pennies between workstations 2 and 3, four pennies between workstations 3 and 4,and four pennies between workstations 4 and 5. This is shown in Figure 2, Step a.

At the beginning of each round, make sure there are six pennies to the left of workstation one. Then,

� Roll the die for workstation one. Record this roll in the Workstation 1 column of the sheet from Appendix 1. Movethe number of pennies showing on the die face from the left of workstation 1 to the right of workstation 1 and addthem to the pennies already to the right of workstation 1. In the MRP model, there is no limit to how many penniescan be placed between any two workstations. For example, if you rolled a four, there are more than four pennies tothe left of workstation 1, so you can move four pennies from the left of workstation 1 to the right of workstation 1.

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This leaves two pennies to the left of workstation 1 and a total of eight pennies between workstations 1 and 2. SeeFigure 2, Step b.

� Roll the die for workstation 2. For example, if there were four pennies between workstation 1 and workstation 2 andyou rolled a six, you could move only the four pennies. You will use this rule (taking the smaller of the die roll ornumber of pennies to the left of a workstation) for each of the four PPC games. Continuing our example, seeFigure 2, Step c.

� Roll the die for workstation 3. This is our constraint, so whatever the die face shows, subtract one and record thisnew number (between 0 and 5) in the Workstation 3 column of the worksheet. Move the smaller of the number ofpennies between workstations 2 and 3 and the die roll minus one. In our example Figure 2, Step d, we rolled a two.Subtract one from the roll of two, giving a one.

� Roll the die for workstation 4 and move pennies appropriately. Continuing our example, see Figure 2, Step e.� Roll the die for workstation 5 and move pennies. In Figure 2, Step f, we continue our example.

Repeat steps (a) through (f) 19 more times to fill out the 20 rows on your sheet (from Appendix 1).Look at how many pennies are to the right of each workstation, and put those numbers in the cells (‘A =’, ‘B =’,

etc. where, for example, A is the number of pennies now sitting between workstation 1 and workstation 2). Add up therolls under workstation 1 and record that at the bottom of the roll as the total.

3.2 JIT/lean system

JIT is a pull system. Each workstation is only allowed to produce if there is a need of downstream. For example, evenif workstation 2 is able to produce items, and there are parts to the left of it waiting to be processed, it will not processunless workstation 3 needs more parts. This is in direct contrast with the push system we saw in the prior scenario. JITlimits the amount of inventory in the system and, ideally, operates with the minimum amount of inventory possible,which is why it is also referred to as ‘lean’.

Figure 2. Example steps of the dice game.

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We will assume that there is constant demand for the product. This means that the market (to the right of worksta-tion 5) is continuously asking for finished goods. In practice, a JIT system would have finished goods inventory andcustomer purchases from finished goods would provide the signal to workstation 5 to produce more. Here, we are notusing finished goods inventory in order to simplify the model and make it consistent with the other PPC models.

JIT systems use a kanban (a visual signal) to authorise the production of parts from upstream workstations. For thisexercise, we will set the kanban to three between each pair of workstations. This means that if any workstation sees lessthan three parts to its right (the queue to the left of the downstream workstation), then it should try to produce partsuntil there are three pieces there. The exception is the market (to the right of workstation 5) which we assume alwaysneeds parts, so there is no kanban amount. To warm up this system, put three pennies to the left of each workstation.See Figure 3.

Workstation 1 should always have three pennies in front of it.Start the 20-day run for this scenario by rolling the die for workstation 5 (and recording it in the Workstation 5

column of a new worksheet). Move the smaller of the number of the die roll and the number of pennies to the left ofworkstation 5 to the right of workstation 5 (indicating parts processed).

Roll the die for workstation 4. The number of pennies to move is the lowest number of these three conditions: (i)the die roll, (ii) the number of pennies to the left of workstation 4 (i.e. between workstations 3 and 4) and (iii) the num-ber of pennies it would take to have three pennies between workstations 4 and 5. For example, assume there was onepenny between workstations 4 and 5 after workstation 5 rolls. The kanban amount is three, therefore, there is authorisa-tion to produce two parts (kanban three minus one in inventory). If the die roll is two or higher, we still are authorisedto produce at most two parts (e.g. a roll of six would still not warrant producing more than two parts). Given that weare authorised to produce two parts, we take the smaller of two parts, the die roll, and the number of parts to the left ofworkstation 4.

Next, roll the die for workstation 3. This is our constraint, so whatever the die face shows, subtract one. Follow thesame procedures described above for workstations 4 and 5 for workstations 1 and 2. Repeat all steps 19 more times tofill out the 20 rows on your sheet then add up the numbers in the first column, excluding the top warm-up row.

3.3 DBR – drum, buffer and rope (TOCs)

The TOCs’ notes that the system can go no faster than the most limited resource, i.e. the constraint. Therefore, materialshould not be released into the system any faster than the constraint can process it. Our constraint is workstation 3. Inthis version of the dice game we will pretend there is a rope or other signal from workstation 3 to the queue to the leftof workstation 1.

To warm up this system, place two pennies between each pair of workstations, except to the left of workstation 3put six pennies. Such a pattern will naturally occur because workstation 3 is slower than workstations 1 and 2, so thoseworkstations can provide parts to workstation 3 faster than it can process them. See Figure 4.

Roll the die for workstation 1 and record the roll on a new worksheet in the Workstation 1 column. Move penniesappropriately. Do the same for workstation 2.

Figure 3. The warmed-up JIT/Lean system starts with three pennies to the left of each workstation waiting to be processed.

Figure 4. The warmed-up DBR or CONWIP system. Two pennies are to the left of each workstation, except for the constraint(workstation 3), which has six pennies.

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Roll the die for workstation 3. This is our constraint, so whatever the die face shows, subtract one. This workstationsends a signal (pulls on the ‘rope’) to release material from your penny jar to the front of workstation 1. Whatever thenumber of pennies you processed on workstation 3, add that same amount of pennies to the queue to the left of work-station 1 from the jar of pennies.

Roll the die and move pennies for workstations 4 and 5. Repeat all steps 19 more times to fill out the 20 rows onyour sheet.

3.4 CONWIP

CONWIP stands for constant work in process. Under CONWIP, when a part is finished on workstation 5 (the end ofthe line), material is released to workstation 1. This keeps a constant amount of inventory in the system.

To warm up this system, use the same set-up from Figure 4 as it did for DBR.Roll the die for each workstation and move pennies as you did in the other games.In CONWIP, for each piece that is finished by workstation 5, a piece is released from the penny jar to the front of

workstation 1. This maintains a constant level of inventory in the system. Whatever the number of pennies that you pro-cess on workstation 5, add that same number of pennies to the left of workstation 1.

4. Excel-based dice game models

The purpose of having students play the manual games is to enable them to understand the underlying logic andmechanics of each PPC system. However, the true differences between the methods cannot be assessed accurately witha single run of each PPC in a dice game. Chance can make the outcome of a single run of the dice game atypical ofthe long run results that a particular PPC method would produce. To allow students to see, and believe, the actual differ-ences in performance between the four production PPC methods, each system must be run multiple times and the resultsaveraged. We do this with Excel-based models of each PPC system. In addition to allowing students to see average sys-tem performance, running Excel-based models after playing the manual dice games allows students to vary the parame-ters, such as mean and standard deviation of processing times and position of the most constrained resource in thesystem, for each PPC system, and see the effects on system throughput and inventory levels over 1000 simulatedproduction runs.

The ‘Input Parameter’ area of each Excel model allows the user to change the following: (i) initial WIP to the left ofeach operation, (ii) production capacity per operation (initially set at 3.5) and (iii) ‘maximum variation around mean’ value(initially 2.5) to represent upper and lower limits of a die roll. Intuitively, given the initial parameters, students generallyguess that each PPC system should be able to produce about 70 (3.5 × 20) parts per month (5 days per week, 4 weeks amonth). The ability to alter the initial WIP to the left of each workstation can alleviate possible student concerns that theplacement of WIP in the manual dice game was somehow rigged to produce a specific outcome. Further, a manual gameplayed once has the constraint in a single location. Students may wonder if having the constraint earlier in the system or ifdifferences in variability (i.e. changing the minimum and maximum values of production at a workstation to reduce vari-ance) would enhance the effects they observed in the manual game. With the ability to quickly run 1000 simulated runswith different parameters, a student will see the differences in inventory and throughput of the four PPC systems.

To run the Excel models, students use F9 on the keyboard on a PC (or FN + F9 on a Mac). The results for ‘ONERUN’ and ‘1000 RUNS’ are updated each time the F9 key is pressed.

4.1 Material requirements planning

In the Excel model, raw material enters the production system at workstation 1 every day according to the roll of a die,which is modelled as an integer value of a random selection from a uniform distribution ranging over 1–6 (or whatevervalues are in the lower limit and upper limit for workstation 1). Each workstation produces the minimum of the numberof parts waiting in the queue and the role of the die, which represents production capacity for that day. When one opera-tion is completed, the product moves to the queue of the next workstation. Figure 5 shows the results of both one runand 1000 runs of 20 days of the MRP model. The 1000 run results show average throughput of 54 units and averageending WIP of 33 units.

4.2 Just-in-time

JIT is a pull system that is in some ways the exact opposite of MRP. JIT is characterised by low WIP between each pairof workstations. In the Excel model, demand flows from right to left, while products flow from left to right and we

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assume that whatever the last operation (workstation 5) produces is purchased by customers, so the model starts with asimulated roll of the die by workstation 5. That workstation produces the lesser of the roll of the die or the number ofitems in WIP between it and workstation 4. Workstation 4, in turn, rolls the die and produces the minimum of threethings – the die roll, the number of items in WIP between workstations 3 and 4 and the number of empty spaces avail-able to put inventory between workstations 4 and 5. Workstations 3, 2 and 1 proceed the same as workstation 4, result-ing in the demand signal flowing upstream from workstation 5 to workstation 1. In the Excel model, the number ofkanbans allowed between each pair of workstations can be varied to illustrate the impact of increasing or decreasingWIP on performance.

Figure 6 below shows the JIT system results. The WIP for 1000 runs of nine units is substantially lower than that ofthe MRP system, which is what JIT/Lean is designed to do. However, only 35 units were shipped in this system.

4.3 DBR (the TOC) system

The TOCs-based system, DBR, assumes that there is a dominant constraint in the system. In the Excel model, the‘desired’ level of WIP is maintained between the first workstation and the constraint. The production rate of the con-straint determines when, and how many, units are released to the first workstation. Figure 7 shows that this systemshipped 50 units, which was less than the MRP system, implying that the bottleneck workstation (workstation 3) insome time periods was starved of inventory to process. The WIP inventory was as low as JIT in this particular example.

4.4 The CONWIP system

CONWIP is a hybrid system that combines features of JIT and TOC. It assumes that there is no dominant constraint.Instead, some ‘desired’ level of WIP is set for the whole plant. Subsequently, the release of raw material to the firstoperation is controlled by the number of units shipped to the end customer. In the Excel-model, the production rate atthe first operation is tied to the production rate of the last operation. Figure 8 shows that CONWIP shipped 49 units,with an average WIP of 10 units.

5. Discussion

In the past, we have had students play the manual and Excel-based dice games using what we was described here asthe MRP system without a bottleneck. These games provide a basis for an interesting discussion of the impact of

Figure 5. Excel worksheet for MRP production system.

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variation, capacity and inventory on throughput (Gupta and Boyd 2011). In this paper, we describe variations of thebasic dice game that provide insight into the logic and mechanics of four different PPC techniques.

In discussing the four systems with the class, the instructor may want to cover points such as:

� PPC systems are measured on the basis of ability to produce finished products (throughput), investment in materials(WIP) and lead time, which is a function of WIP. Therefore, a system that produces more products with less inven-tory is best.

Figure 6. Excel worksheet for JIT production system.

Figure 7. Excel worksheet for DBR production system.

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� The high variability of each operation time (die roll) would typically rule out JIT as a preferred method. Efforts toreduce variability through programmes, such as Six Sigma can be used to reduce variability and make a productionsystem more suitable to JIT logic. Students may be encouraged to run the JIT system with reduced variability andobserve the effect on throughput and inventory.

� MRP allows the system to maximise throughput, but at the expense of high inventory (and thus high lead time).� CONWIP and DBR have throughput almost (or as) high as MRP, but with substantially lower inventory. It appears

that in the simple line design used here, CONWIP and DBR are able to handle high variability environments betterthan traditional MRP or JIT.

We have conducted a pilot test of the manual versions of the games outlined here. The test was conducted near theend of the introductory operations management class after students have read The Goal and seen some references to JITand lean manufacturing, but prior to any detailed discussion or comparison of the four PPC systems. We gave pre- andpost-tests of students’ knowledge of the differences between the systems, and saw significant improvement resultingfrom having played the manual versions of the four dice games. Results of the pre-test (n = 42) and post-test (n = 56)are shown in (Table 1) below. Correct responses on the pre-test averaged 34%, ranging from 12% (on question 1 regard-ing CONWIP) to 55% (on question 4 regarding JIT), with an improvement in responses from the pre-test to the post-testranging from 34% (for question 4) to 470% (for question 1). All of the questions were multiple choice, with responses(a) through (d) for questions 1 through 6 corresponding to the four PPC systems and (e) being ‘None of the above’.Question 7 allowed more than one system to be selected, and question 8 relates only to the DBR system.

The results of the pre- and post-tests indicate that students’ understanding of the four systems improves significantlyjust from reading the instructions and playing the manual dice games prior to any class discussion.

Table 1 shows the pre- and post-test results for eight questions relevant to the characteristics of the four PPC sys-tems. As can be seen from the table, playing the manual dice games resulted in significant increase in knowledge in theareas assessed. For example, Question 6 asks ‘In which of the following systems does WIP inventory build up the mostover time if the system is allowed to operate by its natural rules’? Twenty-nine percent of the class answered this ques-tion correctly on the pre-test, but 69% answered correctly after playing the dice games.

We have used both the manual dice games and the Excel-based games separately with very good results but havenot yet combined the two approaches in one class. However, learning theory suggests that combining the approaches

Figure 8. Excel worksheet for CONWIP production system.

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would result in enhanced student learning. Pashler and his colleagues (2007) summarise the research supporting a num-ber of recommendations in learning theory and find moderate or strong support in the literature for two recommenda-tions that would apply to the approach suggested here: (1) connecting and integrating abstract and concreterepresentations of concepts and (2) helping students build explanations by asking and answering deep questions. Dunlo-sky et al. (2013) also support the latter recommendation, referring to the technique as ‘elaborative interrogation’. Withrespect to the first recommendation, the manual dice games provide students with concrete representations of conceptswhich can then be linked to the more abstract representations in the Excel-based models. In addition, we have studentsread The Goal (Goldratt and Cox 2012) prior to playing either the manual or Excel-based games.

The second recommendation of learning theory, helping students build explanations by asking and answering deepquestions, can be incorporated either in the classroom discussion of the four systems, in a discussion board thread or ina written assignment following the students’ playing the manual and Excel-based games by asking students to changevarious parameters, e.g. WIP level and variability present in the system. We believe this step is especially important toensure that students do not just follow the instructions without attempting to understand how each system operates.

6. Conclusions and future research directions

In this paper, we have developed both a set of manual dice games and Excel-based games for students to play thatallow them to experience the mechanics of four commonly used PPC systems (Note: We will provide the Excel-basedgames on request).

Because the systems discussed here are small (five workstations), the results of the Excel models are strongly influ-enced by the initial inventory placement between workstations. Therefore, it is difficult to draw conclusions about therelative performance of the four PPC systems from these pedagogical models. In view of this, the intended use of themodels presented in this paper is to enhance student understanding of the mechanics of various PPC systems. Sincemost real-world PPC implementations occur in complex manufacturing environment where crucial trade-offs existamong factors, such as variability, inventory and capacity, we propose that in the future the impact of these factorsshould be investigated using the games and the significant effects of these factors on manufacturing performance beevaluated.

We have not combined the use of the manual dice games with the Excel-based games in one class, or compared theimpact on student learning of just using the manual games to just using the Excel-based games, to the combination ofboth. Since each of these assignments requires time on the part of students and may displace other assignments, itwould be useful to know the extent to which learning is enhanced by the combination of the approaches over either oneof them.

Last but not the least, the games discussed in this paper assumed repetitive manufacturing environments; instructorsare encouraged to generate more meaningful discussions in broader context. For example, does the chosen business andmanufacturing strategies dictate the PPC approach a company might choose (e.g. low variability at Toyota’s operationslends itself well to JIT)?

Table 1. Results of pre- and post-tests.

Correct

No. QuestionPre-test(%)

Post-test(%)

Improvement(%)

1 Releasing raw material to the first operation once the last operation finishes a part is acharacteristic of which of the following systems?

12 68 470

2 Having unlimited raw material available to the first operation is a characteristic of whichof the following systems?

40 75 85

3 Releasing raw material to the first operation once the bottleneck operation finishes a batchof parts is a characteristic of which of the following systems?

43 66 54

4 Limiting the maximum inventory between workstations is a characteristic of which of thefollowing systems?

55 73 34

5 Allowing each operation to process as much as it can is a characteristic of which of thefollowing systems?

31 84 171

6 In which of the following systems does WIP inventory buildup the most over time if thesystem is allowed to operate by its natural rules?

33 68 104

7 Which of the following systems would be considered a low-inventory system? 24 48 1038 In DBR, the rope is tied from what to what? 33 66 98

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Appendix 1. Manual game formSee Figure 9.

Figure 9. Student worksheet for the four manual games.

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